frame-interpolation
latent-diffusion
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frame-interpolation | latent-diffusion | |
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74 | 70 | |
2,672 | 10,575 | |
3.0% | 5.4% | |
0.0 | 0.0 | |
8 months ago | 2 months ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | MIT License |
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frame-interpolation
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Aging with AI from age 9 to age 99.
- Lastly I used FILM, an image interpolation library to interpolate between images
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AnimDiff
1) generate video using https://github.com/camenduru/animatediff 2) upscale using SD-CN https://github.com/volotat/SD-CN-Animation 3) interpolate frames using https://github.com/google-research/frame-interpolation 4) add audio using https://huggingface.co/spaces/suno/bark
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What is the current best way to make sequence images for animation that keep the art style consistent?
I am aware of interpolation as well (https://github.com/google-research/frame-interpolation) where you give it two images and it generates the images in between to get there but not sure I have good enough images to attempt to use this yet.
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The AI will make You an Anime in Real Time
Super neat though. With some interpolation (possibly this Google Research one I just found via ChatGPT), it wouldn't be too bad to dump a video in and have it process in the background.
- my older video, without controlnet or training
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The secret to REALLY easy videos in A1111 (easier than you think)
FILM repo by Google Research, they made this very cool interpolation method, my favourite so far. It's a pain to set up, didn't manage to run it on my local machine, I'm not very smart, and I can't get "pip install tensorflow==2.6.2" to run on my Windows, so can't run the requirements, so can't run the script.. BUTTTT you can use colab here, and once you hook it up to your GDrive, you can change the path to your folder of images, and it will process and spit out the interpolated video for you. I only have free tier, and it took 16 minutes for the sample video.
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Loopback Wave Workflows (FILM, AE, Flowframes)
FILM (Frame Interpolation for Large Motion)
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More Loopback Wave + Flow, this time with realistic people
Edit: used this for the interpolation. Flow wasn't the correct word. https://github.com/google-research/frame-interpolation
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Large Motion Frame Interpolation – Google AI Blog
Also off-topic, but their github.io page has a bibtex snippet for anyone wanting to cite their work in their papers. I'm not an academic, but I still strangely appreciate the gesture.
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AI Video to Fill Missing Frames/Smooth Animation?
FILM? https://film-net.github.io/
latent-diffusion
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SDXL: The next generation of Stable Diffusion models for text-to-image synthesis
Stable Diffusion XL (SDXL) is the latest text-to-image generation model developed by Stability AI, based on the latent diffusion techniques. SDXL has the potential to create highly realistic images for media, entertainment, education, and industry domains, opening new ways in practical uses of AI imagery.
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Is it possible to create a checkpoint from scratch?
Here's a link to the early latent-diffusion git, that might be able to create a blank model (I haven't tested it): https://github.com/CompVis/latent-diffusion
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Anything better than pix2pixHD?
Latent diffusion could work for you: https://github.com/CompVis/latent-diffusion (https://arxiv.org/abs/2112.10752)
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Image Upscaler AI
There are a lot but the one implemented as LDSR in most stable guis is this one. https://github.com/CompVis/latent-diffusion
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I've been collecting millions of images of only public domain /cc0 licensing. I'd like to train a stable diffusion model on the collection. Could some one share their knowledge of what this would take? Otherwise, simply enjoy my library.
CompVis/latent-diffusion: High-Resolution Image Synthesis with Latent Diffusion Models (github.com)
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Run Clip on iPhone to Search Photos
The "retrieval based model" refers to https://github.com/CompVis/latent-diffusion#retrieval-augmen..., which uses ScaNN to train a knn embedding searcher.
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Class Action Lawsuit filed against Stable Diffusion and Midjourney.
Stability is basically https://github.com/CompVis/latent-diffusion + training data.
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[D] Influential papers round-up 2022. What are your favorites?
Found relevant code at https://github.com/CompVis/latent-diffusion + all code implementations here
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Can anyone explain differences between sampling methods and their uses to me in simple terms, because all the info I've found so far is either very contradicting or complex and goes over my head
DDIM and PLMS were the original samplers. They were part of Latent Diffusion's repository. They stand for the papers that introduced them, Denoising Diffusion Implicit Models and Pseudo Numerical Methods for Diffusion Models on Manifolds.
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AI art is very dystopian.
yes, https://github.com/CompVis/latent-diffusion
What are some alternatives?
ebsynth - Fast Example-based Image Synthesis and Style Transfer
disco-diffusion
AnimeInterp - The code for CVPR21 paper "Deep Animation Video Interpolation in the Wild"
dalle-mini - DALL·E Mini - Generate images from a text prompt
sd-webui-mov2mov - This is the Mov2mov plugin for Automatic1111/stable-diffusion-webui.
dalle-2-preview
VQGAN-CLIP-Video - Traditional deepdream with VQGAN+CLIP and optical flow. Ready to use in Google Colab.
hent-AI - Automation of censor bar detection
optical.flow.demo - A project that uses optical flow and machine learning to detect aimhacking in video clips.
stable-diffusion
frame-interpolation - FILM: Frame Interpolation for Large Motion, In arXiv 2022.
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch